ralph-wiggum-codex

Automate iterative AI coding tasks by running repeated codex exec calls that save progress to a persistent state file between iterations, allowing the workflow to pause and resume without losing context.

Yes, you can define explicit stop conditions for AI agent workflows using custom completion promises or by setting a maximum iteration limit to ensure deterministic task completion and prevent infinite loops.

Iterative AI task execution is a process where an AI agent runs tasks in a loop, evaluating its progress towards a goal. You need it for complex coding or content generation workflows requiring multiple steps and feedback loops.

Start an iterative AI workflow by invoking the runner with a specific prompt file, defining a maximum iteration count, and setting a completion promise keyword to trigger the explicit stop condition for your task.

Yes, the runner supports resuming workflows after interruptions by utilizing a persistent state file that saves progress between each iteration, ensuring long-running processes can recover and continue seamlessly.

Iterative AI agents for automated coding tasks require explicit stop conditions or maximum iteration counts to prevent unbounded loops, and managing the persistent state file is necessary to maintain progress tracking correctly.